An efficient learning based approach for automatic record deduplication with benchmark datasets.

With technological innovations, enterprises in the real world are managing every iota of data as it can be mined to derive business intelligence (BI). However, when data comes from multiple sources, it may result in duplicate records. As data is given paramount importance, it is also significant to eliminate duplicate entities towards data integration, performance and resource optimization. To realize reliable systems for record deduplication, late, deep learning could offer exciting provisions with a learning-based approach. Deep ER is one of the deep learning-based methods used recently for dealing with the elimination of duplicates in structured data. Using it as a reference model, in this paper, we propose a framework known as Enhanced Deep Learning-based Record Deduplication (EDL-RD) for improving performance further. Towards this end, we exploited a variant of Long Short Term Memory (LSTM) along with various attribute compositions, similarity metrics, and numerical and null value resolution. We proposed an algorithm known as Efficient Learning based Record Deduplication (ELbRD). The algorithm extends the reference model with the aforementioned enhancements. An empirical study has revealed that the proposed framework with extensions outperforms existing methods.

An improved method for quality control of in situ data from Argo floats using α convex hulls.

An improved method for detecting abnormal oceanic in situ temperature and salinity (T/S) profiles is developed. This procedure extends previous method developed by Udaya Bhaskar et al. [2017]. This method utilizes World Ocean Atlas 2013 gridded climatology which is on 0.25° x 0.25° resolution to build α convex hulls. These α shapes are then used to categorize good and bad in situ T/S data profiles. This extended method classify the entire profiles instead of data for standard depths to avoid any errors introduced by interpolation to standard depths. Like in previous method, an 'n' sided polygon (convex hull) encompassing the T/S profile data is constructed using Jarvis March algorithm and Points In Polygon (PIP) principle is employed to judge the profile as good or bad. Extensive sensitivity experiments were done for arriving at the optimal α value such that false positives and true negatives are minimized. All types of issues associated with the in situ oceanographic data are identified and quality flag assigned. Examples of this improved method as applied to few Argo floats are presented.•The T/S profiles corresponding to region of interest are used to build α convex hulls.•This extended method can be effectively used for quality control of entire profile and clearly demarcate the profile as good/bad.•This method has the advantage of treating bulk of oceanographic in situ profiles data in a single go which filters out erroneous profile data from the good.

Characterization of oceanic Noctiluca blooms not associated with hypoxia in the Northeastern Arabian Sea.

Intense blooms of the heterotrophic dinoflagellate, green Noctiluca scintillans, have been reported annually in the Northern Arabian Sea since the early 2000s. Although not known to produce organic toxins, these blooms are still categorized as a harmful due to their association with massive fish mortalities. Recent work has attributed these blooms to the vertical expansion of the oxygen minimum zone, driven by cultural eutrophication from major coastal cities in western India. As diatoms are preferred prey of green Noctiluca scintillans, more frequent blooms of this mixotroph will likely impact the productivity of important fisheries in the region. The present study uses a satellite algorithm to determine the distribution of both diatom and green Noctiluca blooms in the Northeastern Arabian Sea from 2009 to 2016. The results from shipboard microscopy of phytoplankton community composition were used to validate the satellite estimates. The satellite algorithm showed 76% accuracy for detection of green Noctiluca and 92% for diatoms. Shipboard measurements and data from biogeochemical-Argo floats were used to assess the relationship between oxygen concentrations and green Noctiluca blooms in the Northeastern Arabian Sea. Regardless of the presence of a Noctiluca bloom, the dissolved oxygen in the photic zone was always >70% saturated, with an average oxygen saturation >90%. The variability in the relative abundance of diatoms and green Noctiluca is not correlated with changes in oxygen concentration. These findings provide no evidence that cultural eutrophication has contributed to the decadal scale shifts in plankton composition in the Northeastern Arabian Sea oceanic waters. Conversely, the climatic warming of surface waters would have intensified stratification, thereby reducing net nutrient flux to the photic zone and decreasing silicate to nitrate ratios (Si:N); both factors that could increase the competitive advantage of the mixotroph, green Noctiluca, over diatoms. If so, the decadal-scale trajectory of phytoplankton community composition in the Northeastern Arabian Sea may be a harbinger of future climate-driven change in other productive oceanic systems.

Pyrolysis of waste electrical and electronic equipment: effect of antinomy trioxide on the pyrolysis of styrenic polymers.

This work has investigated the effect that antimony trioxide has on the pyrolysis of styrenic polymers and the effect that different types of brominated flame retardants used in plastics have on the composition of the pyrolysis products. Brominated high impact polystyrene (Br-HIPS) which contained either 5% or 0% antimony trioxide and either decabromodiphenyl oxide (DDO) or decabromodiphenyl ethane (DDE) was pyrolysed in a fixed bed reactor at 430 degrees C. Some experiments on the fixed bed reactor involved mixing the Br-HIPS with polystyrene. The gaseous products were analysed by GC-FID and GC-TCD and it was found that antimony trioxide caused an increase in the proportion of ethane and ethene and suppressed the proportion of butane and butene. When DDE was the flame retardant increased proportions of ethane and ethene were found in the pyrolysis gas compared to when DDO used. When polystyrene was mixed with the Br-HIPS it suppressed the trends observed in the gas composition during the pyrolysis of Br-HIPS. The pyrolysis oils were characterised using FT-IR, GC-MS, GC-FID, and GC-ECD. It was found that the plastic which did not contain antimony trioxide pyrolysed to form mainly toluene, ethylbenzene, styrene, cumene, and alpha-methylstyrene. The oils produced from the pyrolysis of the plastic that contained antimony trioxide did not contain any styrene or alpha-methylstyrene, but instead contained greater concentrations of ethylbenzene and cumene. The absence of styrene and alpha-methylstyrene from the pyrolysis oil occurred even when the Br-HIPS was mixed with polystyrene. GC-ECD analysis of the oils showed that the plastics which did not contain antimony trioxide pyrolysed to form (1-bromoethyl)benzene, which was totally absent from the pyrolysis oils when antimony trioxide was present in the plastic.

Quality control of oceanographic in situ data from Argo floats using climatological convex hulls.

A new method of identifying anomalous oceanic temperature and salinity (T/S) data from Argo profiling floats is proposed. The proposed method uses World Ocean Database 2013 climatology to classify good against anomalous data by using convex hulls. An n-sided polygon (convex hull) with least area encompassing all the climatological points is constructed using Jarvis March algorithm. Subsequently Points In Polygon (PIP) principle implemented using ray casting algorithm is used to classify the T/S data as within or without acceptable bounds. It is observed that various types of anomalies associated with the oceanographic data viz., spikes, bias, sensor drifts etc can be identified using this method. Though demonstrated for Argo data it can be applied to any oceanographic data. •The patterns of variation of the parameter (temperature or salinity) corresponding to a particular depth, along the longitude or latitude can be used to build convex hulls.•This method can be effectively used for quality control by building Convex hulls for various observed depths corresponding to biogeochemical data which are sparsely observed.•This method has the advantage of treating the bulk of oceanographic in situ data in a single iteration which filters out anomalous data.